AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation

TL;DR

This paper introduces AdaCropFollow, a self-supervised online adaptation method that enables under-canopy agricultural robots to maintain accurate navigation across varying field conditions without human intervention.

Contribution

It presents a novel self-supervised adaptation approach using a visual foundation model and pseudo labeling to improve perception in autonomous agricultural robots.

Findings

Effective adaptation with minimal data

Improved navigation accuracy in diverse conditions

Enables fully autonomous row-following

Abstract

Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work, we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this paper, we propose a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention.